Dynamic RAT Selection and Transceiver Optimization for Mobile Edge Computing Over Multi-RAT Heterogeneous Networks

TL;DR

This paper proposes a joint RAT selection and transceiver optimization framework for multi-RAT MEC systems, aiming to reduce error, transmission costs, and delay in IoT applications.

Contribution

It introduces a novel low-complexity algorithm for joint optimization in multi-RAT MEC systems considering energy and delay constraints.

Findings

The proposed algorithm outperforms existing schemes in simulation.

Joint optimization significantly reduces MSE and transmission delay.

Effective RAT and transceiver design enhances MEC system performance.

Abstract

Mobile edge computing (MEC) integrated with multiple radio access technologies (RATs) is a promising technique for satisfying the growing low-latency computation demand of emerging intelligent internet of things (IoT) applications. Under the distributed MapReduce framework, this paper investigates the joint RAT selection and transceiver design for over-the-air (OTA) aggregation of intermediate values (IVAs) in wireless multiuser MEC systems, while taking into account the energy budget constraint for the local computing and IVA transmission per wireless device (WD). We aim to minimize the weighted sum of the computation mean squared error (MSE) of the aggregated IVA at the RAT receivers, the WDs' IVA transmission cost, and the associated transmission time delay, which is a mixed-integer and non-convex problem. Based on the Lagrange duality method and primal decomposition, we develop a low-complexity algorithm by solving the WDs' RAT selection problem, the WDs' transmit coefficients optimization problem, and the aggregation beamforming problem. Extensive numerical results are provided to demonstrate the effectiveness and merit of our proposed algorithm as compared with other existing schemes.